In network-based computing structures, most generally comprising a plurality of computer-based objects and associated relationships among some or all of the objects, it is often the case that it is desirable to conveniently cluster subsets of the network for various purposes, such as improving user navigation. It may also be desirable to cluster or create affinity groups of other system aspects or elements, such as system users. However, historically, automatically clustering large networks of objects has been computationally intensive, to the point of being prohibitively time consuming for many otherwise desirable clustering applications. Further, prior art approaches to clustering information in network-based computing structures have exhibited arbitrariness in the outcomes of the clustering process due to dependence on the initial selection of the loci of clusters. The process for initializing clustering loci in prior art approaches typically has required either random selection of cluster loci or some degree of manual intervention. The former approach requires many separate runs with different selected foci to achieve reasonable results, often being computationally prohibitive, and the latter approach also does not necessarily ensure best results, and, of course, is not a fully automatic. Further the prior art does not address effective and efficient clustering procedures that are not only automatic, but that are adaptive to system use. The lack of adaptation to use of computer-based system clustering results in “brittle” clustering approaches that fail to deliver a high degree of value to systems users on a sustained basis.
Therefore a computationally more efficient and less manual system and method of clustering network-based computing structures, and one that is also adaptive to system use, is highly desirable.